Abstract
Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.
Highlights
The use of biometrics for access control in restricted infrastructures has been extensively researched during the last 4 decades
The proposed framework was evaluated on the publicly available ACTIBIO dataset. This dataset was captured in an Ambient Intelligence (AmI) indoor environment and its recordings include 29 subjects, performing a series of office/workplace activities and walking in both straight and arbitrary paths for eight repetitions each
Behavioral biometrics are very valuable for many realistic application scenarios that require identification or authentication of an individual
Summary
The use of biometrics for access control in restricted infrastructures has been extensively researched during the last 4 decades. Common physical biometrics include fingerprints; hand or palm geometry; and retina, iris, or facial characteristics. Current biometric security systems exhibit various shortcomings despite their wide acceptance in commercial applications[1,2]. One such shortcoming is the de facto exclusion or discrimination of people by the system whose biometrics cannot be recorded well for the creation of the database reference (e.g., people whose fingerprints do not print well or people who miss the required limb or feature). The research on new biometrics that use features that exist in every human, rendering them applicable to the greatest possible percentage of the population, becomes very important
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